An MCDA-based patient decision aid for patients with bipolar disorder
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How can patients be enabled to participate in decisions concerning their health? How can decisions be improved, concordent with patient values? A new patient decision aid will capitalise on network meta-analysis and single subject research designs to foster better decisions.
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An MCDA-based patient decision aid for patients with bipolar disorder
- 1. A patient decision aid for bipolar disorder Full title: MCDA-Based Support in Clinical Decision- Making Throughout the Patient Journey: The Use of ALBA in Bipolar Disorder Øystein Eiring, MD, specialist in psychiatry, cand. mag, PhDc. Editor Norwegian Electronic Library of Health/Mental Health Head of Department of Knowledge Services, Innlandet Hospital Trust
- 2. My affiliation The community developing MCDA- based, Annalisa patient decision aids
- 4. The Norwegian Electronic Health Library 10/2/201 4
- 5. • Free access • for clinicians and patients • nation-wide Guidelines Journals Databases Nylenna M, Eiring Ø, Strand G, Røttingen JA. Textbooks Wiring a nation: Putting knowledge into action. Lancet 2010; 375: 1048–51 10/2/201 5
- 7. The University of Oslo (enhanced)
- 8. Menu
- 9. Menu Context Three problems (and solutions) Summary
- 10. Patient decision aids • Tools for personalised decisions • Many personalisation technologies • Very limited use of technologies Eiring Ø, Slaughter L. An Assessment of the Potential for Personalisation in Patient Decision Aids. Lecture Notes of The Institute for Computer Sciences, Social Informatics and Telecommunications Engineering Volume 91, 2012, pp 51 - 57
- 11. Bipolar disorder • Singapore: 1,2 % • Cross-national 0.3 – 1.5% Chong SA, Abdin E, Vaingankar JA, Heng D, Sherbourne C, Yap M, Lim YW, Wong HB, Ghosh- Dastidar B, Kwok KW, Subramaniam M. A population-based survey of mental disorders in Singapore. Ann Acad Med Singapore 2012 Feb;41(2):49-18
- 12. Bipolar disorder I • Chronic • Suicide risk • Relapses • ”Never well” • Impairment • Medication mainstay in treatment
- 13. Outpatient psychiatry in Hamar, Norway
- 15. Why can´t I stop my medication? It really bothers me. I would be much better without! With permission. iStockphoto. The person depicted has no relation to the subject and the picture is for illustrative purposes only.
- 16. You must continue taking it! If not, you will certainly have a relapse again. You don´t remember how bad it was, but I do
- 17. (Not shared decision- making)
- 19. Why can´t I stop my medication? It really bothers me. I would be much better without!
- 20. Ok, lets try to find the best decision together What are the benefits and harms you care about? And how likely are they, with and without medication?
- 21. Three patient roles Consumer Shared decision-making Doctor knows best Stubblefield C, Mutha S. Provider-patient roles in chronic disease management. J Allied Health. 2002 Summer;31(2):87-92.
- 22. The goal • Find - together • the best decision • for you
- 23. And to achieve the goal… • Find the: • options • attributes • probabilities • preferences
- 24. In example, hypothetical: Options Suicide Relapse Nausea risk risk risk Medicine A 2 % 25 % 10 % Medicine B 1 % 35 % 1%
- 25. Four sources of knowledge All the research in the world Electronic medical record
- 26. Problem 1
- 27. Knowledge not tailored to decisions • All options not directly comparable • Not personalised All the research in the world • Not always reliable • Not always readable • Tells nothing in itself, without preferences
- 28. A possible solution: Network meta-analysis
- 29. Top of the 6S model In EMR Readable Summary Relevant Reliable Network m.a. Systematic reviews High quality single studies Single studies Dicenso A, Bayley L, Haynes RB. Accessing pre-appraised evidence: fine-tuning the 5S model into a 6S model. Evid Based Nurs. 2009 Oct;12(4):99-101
- 30. A network of studies Vergel YB, Dunn G, Palmer S, Beynon s, Woolacott N, Soares-Weiser K, Geddes J, Gilbody S. A Simultaneous Comparison of Multiple Treatments for Bipolar I: An Application of Bayesian Statistical Methods. Poster
- 31. Direct and indirect comparisons Vergel YB, Dunn G, Palmer S, Beynon s, Woolacott N, Soares-Weiser K, Geddes J, Gilbody S. A Simultaneous Comparison of Multiple Treatments for Bipolar I: An Application of Bayesian Statistical Methods. Poster
- 32. Part 1 of the work • Complete two network meta-analyses
- 33. Problem 2
- 34. Knowledge not operational • Information about the effects of medicines taken is • not systematic Electronic medical record • not quantified • (Often lacking) • not structured • not available to the patient
- 35. A possible solution: An Annalisa decision aid
- 36. Enabling continuous registration of attributes • Example: depressive symptoms • Patient registers level of depression weekly • Feeds into the decision aid • Assessment of the effect of the medication • continuously and in retrospect
- 37. Will visualise changes over time
- 38. Part 2 of the work • Developing the Annalisa decision aid
- 39. Problem 3
- 40. How to evaluate a personalised intervention?
- 41. The problem with group designs • Take considerable time and resources • No improvement of intervention on-the-fly • Do not establish causality in the individual Kazdin AE. Single-Case Research Designs: Methods for Clinical and Applied Settings, 2nd Edition. New York, Oxford. 2009.
- 42. A possible solution: single-subject design
- 43. Single subject designs • Extensive use within behavioural sciences • Internal validity maintained • Mimics and feasible within clinical practice • Not the same as case studies!
- 44. Benefits • Assesses effect in the individual patient • Identifies causal relationships • External validity comparable to group designs • Determines efficacy in novel interventions • Helps optimalise the intervention • Design and intervention can be adjusted on the fly
- 45. ABAB design
- 46. Non-concurrent, multiple baseline design
- 47. Combined
- 48. 3 novelties in this work
- 49. Novelty 1: Complete NMAs and feed the values into the desicion aid
- 50. Novelty 2: • Continuous registration of attributes important to the patient • in a decision aid engineered for distributed decisions
- 51. Novelty 3: • Utilising a single subject research design… • to evaluate a patient decision aid
- 52. Flexible yet rigorous A personalisable: • decision aid • assessment tool • study design
- 53. Thank you